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Twelve real frames from the fixture set now align in 4.5 s — 4.4 s of matching, 118 ms of bundle adjustment — where the first run took 51 s and left the first two frames out. The matcher computes each pair's similarity matrix once, across the cores, with a dot product written to vectorise; both nearest-neighbour directions read it. The frames that failed were portrait: fitted into the landscape input they used 512 of 1024 px, and their thin overlap did not survive at half resolution. The same weights are now exported at 768×1024 as well and the detector picks the shape by aspect. The example aligns from embedded previews and draws the set on a cylinder; on the fixture the sweep is 152° at a fitted 47.9 mm against the EXIF's 50, RMS 1.5 px, and the overlaps show no ghosting.
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Model weights — licensing
Two Ultralytics checkpoints ship here, both exported by
tools/export-seg-model.sh, each with its class vocabulary written out by the
same script:
| File | Checkpoint | Trained on | Used by |
|---|---|---|---|
segment/yolo26n-seg.onnx |
yolo26n-seg.pt |
COCO, 80 thing classes | local adjustments, subject selection |
scene/yolo26s-sem-ade20k.onnx |
yolo26s-sem-ade20k.pt |
ADE20K, 150 classes | the scene tab's per-category grades |
Both come from https://huggingface.co/Ultralytics/YOLO26. The face weights in
face/ are a separate matter with a separate grant — see face/README.md.
The keypoint weights in keypoints/ are a third, and the easiest — see the
last section.
The grant
Ultralytics releases YOLO under AGPL-3.0, and the weights carry the same
grant as the framework — the HuggingFace repository declares agpl-3.0 for the
checkpoints themselves, not merely for the training code. A commercial licence
is offered separately; DarkRoom does not use it and does not need it.
What that means for DarkRoom
DarkRoom is GPL-3.0-or-later. GPLv3 §13 explicitly permits combination with AGPL-3.0 code, so redistributing these weights inside this repository is allowed — this is not the situation the InsightFace "buffalo" weights would have created, where a non-commercial research grant is simply incompatible with the project's licence and with F-Droid, Flatpak and Play distribution (NFR-COMPAT-2, D13).
The consequence, and it is a real one: the combined work is effectively AGPL-3.0. §13's permission runs one way — the AGPL's §13 network-use condition attaches to the portion under that licence. For a local-first desktop and Android photo editor that condition has no practical bite, because there is no network service offering the combined work to remote users. It would acquire bite the moment any hosted or server-side rendering appeared, and that is the thing to remember rather than rediscover.
This was decided deliberately (D14), not arrived at by accident, and
docs/segmentation.md §7 records the reasoning.
Class vocabulary — a caveat worth reading
docs/segmentation.md §4 specified YOLO pretrained on ADE20K, whose 150
classes include the stuff categories that matter most in photography — sky,
vegetation, water, wall, mountain.
This was true when written and is not any more. Checked 2026-08-21, no
YOLO/ADE20K combination existed: Ultralytics shipped YOLO26-seg on COCO
only, and the one HuggingFace repository claiming otherwise
(laxmacl/yolov8-ade20k) was empty. Re-checked 2026-08-30: Ultralytics now
ships a semantic task with ADE20K checkpoints
(https://docs.ultralytics.com/tasks/semantic), and yolo26s-sem-ade20k is
what scene/ holds.
So the two vocabularies divide the work rather than compete:
segment/, COCO, 80 things. Separates instances — clicking one of three people selects that person. This is what local adjustments need, and a semantic model cannot do it: it would return one "person" region covering all three.scene/, ADE20K, 150 classes. Labels every pixel, including the stuff COCO has no word for — sky, vegetation, water, mountain, wall. This is what the scene tab's per-category grades need, and it does not care that instances are merged, because a per-category grade applies to the whole category.
Neither replaces the other. Keeping both is the deliberate choice.
The loader treats each vocabulary as model metadata rather than compiled-in knowledge, which is what made adding the second model a file plus a descriptor rather than a code change — as this document predicted it would be.
keypoints/ — XFeat, Apache-2.0
| File | Source | Trained on | Used by |
|---|---|---|---|
keypoints/xfeat-1024.onnx |
weights/xfeat.pt from https://github.com/verlab/accelerated_features |
MegaDepth + synthetic warps, by the authors | panorama alignment (FR-MRG-8), landscape frames |
keypoints/xfeat-768.onnx |
the same weights | — | the same, portrait frames |
Exported by tools/export-xfeat.sh at fixed grayscale inputs of 1024×768
and 768×1024 — the same weights twice, because tract needs a static shape
and a portrait frame in a landscape input wastes half of it. Only the
convolutional network is in each file; the keypoint decoding is Rust.
The repository and its weights are Apache-2.0, read on 2026-09-19 from the
LICENSE at its root, with no separate grant on the checkpoint and no
non-commercial clause anywhere in the tree. Apache-2.0 is GPLv3-compatible
one way — code and weights under it may be combined into a GPLv3 work — so
this is neither the InsightFace situation (D13, a use restriction that binds
every user) nor the Ultralytics one (D14, where the combined work becomes
AGPL). It is the licence position this document would have wanted for every
model in it, and it was chosen over stronger detectors partly for that reason:
SuperPoint and SuperGlue are non-commercial, R2D2 and SiLK are CC BY-NC.
The training data is the authors' concern, not a licence on the weights: XFeat trains on MegaDepth, which is itself a research dataset, but the weights are released under the repository's licence without a data-derived restriction — unlike the gaze models §7 of the requirements declined, where the dataset licence restricts models trained on it by name.